PET/CT Radiomics in Breast Cancer: Promising Tool for the Prediction of the Ki67 Expression

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Abstract Objective: This study aims to examine the values of radiomics parameters derived from 18-fluorine-fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT) imaging in the prediction of ki-67 expression in breast cancer patients.Patients and methods: A total of 115 patients diagnosed with breast cancer and examined by 18F-FDG PET/CT were included in this study. The Ki-67 proliferation index was determined from the pathological specimen as positive or negative. Radiomics features were extracted by pyRadiomics and reduced by Independent t-test and least absolute shrinkage selection operator. The radiomics risk score (RRS) was calculated with all the selected features. RRS incorporated with clinical-pathological features were used to construct a binary logistic regression and nomogram classifier. Receiver operating characteristic curve (ROC) analysis was used to predict the accuracy. Decision curve analysis (DCA) was performed to assess clinical utility. Results: Totally 944 features were reduced to 14 predictors. RRS were significantly differed between the ki67+ and ki67- groups (0.440 ± 0.473 and 1.039 ± 0.430; t = -6.663, p < 0.001). In the binary logistic regression, N stage (OR [95%CI], 5.752 [2.032, 16.286], p<0.001) and RRS (OR [95%CI], 20.540 [5.521, 76.423], p<0.001) were independent factors in predicting Ki67 expression. In ROC analysis, AUC was 0.866 (0.790, 0.922), (p<0.001), with sensitivity, specificity, Youden index and cutoff value of 82.50%, 80.00%, 0.6250 and 0.6672, respectively. DCA indicated that use of the clinical-radiomic nomogram had more benefit than utilizing either clinical or radiomic features alone.Conclusion: The radiomics-derived evaluation score combined with N stage could effectively predict Ki67 expression in breast cancer, enabling proper patient selection for treatment.

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